When designers make compromises during the engineering design process the result is often

8.3 Design process

Matt Hunter, Chief Design Officer, Design Council (2016) proposes a “Double Diamond Model,” in which design process is divided into four distinct phases: Discover, Define, Develop, and Deliver (see Fig. 8.1), which begins with wide research and thinking and through definition is narrowed down to a focus on design objectives to deliver a viable design solution or product.

In the primary phase “Discover,” at the start of the process, designers seek inspiration, gathering information on what is new and exciting through market intelligence, user inquiry, mind mapping, and design research collectives. In the second phase “Define,” designers look at possibilities the Discover phase has identified and establish which are the most important priorities and in what order they are addressed. Definition thus determines the design brief and presents the challenges to the design/development team. The third phase “Develop” is when prototypes are developed, tested, revisited, and refined; activities during the development phase include prototyping, multidisciplinary approaches, and establishing test methods. During the final phase of “Deliver,” feedback is gathered, prototypes are selected and approved, and products are finalized and launched (Hunter, 2016). Apparel design for functional and performance clothing similarly takes a phased or multistep approach to design and new product development.

Product development, sometimes referred to as “new product development” (NPD), is defined as a series of steps from concept, design, and development to the marketing, production, and management of a new product.

Many frameworks, models, and theories have been formulated in the design and development of clothing products and these have informed the product development process over time (Laurel & Young-A, 2016). This chapter will examine those related primarily to design and development of performance apparel and functional clothing (LaBat & Sokolowski, 1999; Lamb & Kallal, 1992; McCann & Bryson, 2014; Rosenblad-Wallin, 1985; Watkins & Dunne, 2015). Models and frameworks used in the development of performance clothing emphasize intensive research and analysis at the start of the process for the designer to meet user needs.

Design process is regarded as creative problem solving (Koberg, 1981) through a series of steps or a sequence of activities, which lead from initial concept to realization. Process varies in the number of stages employed by designers; however, all begin with research in the first instance, which helps to define problems and establish design criteria. The steps within the design process may be revisited many times to reach a satisfactory clothing solution. There are actions within the design process, which are critical to successful development of high performance and functional clothing as outlined in the five-step model presented by Watkins and Dunne (2015):

Research—Definition—Idea Generation—Design Development—Evaluation

Design of high performance and functional clothing is complex and it is incumbent on the designer to research, in detail, about the user, the activity, the environment, and any hazards, which the wearer may encounter. Thorough research at the start of the process highlights any constraints that may influence design thinking, such as limitations of the body, constraints of the use situation, and any legislative and regulatory restrictions relevant to the activity. Full exploration and subsequent analysis of the elements previously described provide the designer with sound comprehension of problems in need of solution. Clear definition of the problem will enable the designer to establish a set of design criteria in response to the brief.

Garment function is of primary importance in the design of performance apparel; however, there are additional considerations surrounding the attractiveness of the garment to the user, as well as how clothing makes the wearer feel during use, which the designer must address to get customer “buy-in.” Lamb and Kallal (1992) present the FEA Consumer Needs Model, a conceptual and integrated framework for apparel design; combining functional, expressive, and aesthetic considerations, which build a profile of the user/consumer at the center of the model. User needs and wants identify problems, which in turn help the designer to establish design criteria, as illustrated in Fig. 8.2 presented here.

Functional needs refer to fit, mobility, comfort, and protection; expressive considerations include, values, role, self-esteem, and status; and aesthetic needs relate to perceptions of beauty, design, and body/garment relationships. Whilst consumers need their garments to function well, expressive values and aesthetic appearance are also important. For a garment designed for particular purpose to appeal to the consumer, clothing must function well and look good from the viewpoint of the user (McCann & Bryson, 2014).

To help designers generate fit-for-purpose solutions, McCann and Bryson (2014) presents the Design Tree Model, which addresses Form (aesthetics and culture), Commercial Realities (product, position, price, promotion), and Function (demands of the body and activity). The Design Tree begins with identification of user needs and subsequent choice of concept, and through areas of Form, Commercial Realities, and Function, directs the designer to consider all aspects of user requirements as illustrated in Fig. 8.3.

Designers and product developers will establish a hierarchy of needs according to the design brief. The FEA model presents a continuum, which is weighted toward either function or aesthetics, for example, survival clothing such as fire fighter's gear, immersion suits, and apparel that provides a barrier against chemical or biological (CB) agents are heavily weighted toward function, with aesthetics of lesser significance. Conversely, occasion wear is weighted toward the aesthetic, with less emphasis on function. High-performance sportswear provides an interesting challenge for the designer as its function is of prime importance; however, as the “athleisure” industry continues to grow, aesthetics have gained prominence in the field, with design features and textile properties adopted by the fashion industry. Expressive needs of role and self-esteem are also important considerations as an athlete must feel confident, whilst a paramedic or first responder must be easily identifiable and instill confidence in their patients; thus, the design process becomes complex, with various user needs requiring consideration. The designer must consider their research and define problems in need of solution; criteria ranked in order of importance, may require a decision as to what is essential and what compromise is required. An example of such a “trade-off” might be impact protection for a number of activities such as skateboarding and BMX cycling; it is essential for joints such as knees and elbows to be protected from fall/impact; however, impact protective pads or clothing inevitably impact mobility. The designer must therefore consider whether protection or mobility are of paramount importance. Equally, body armor used by the police and military personnel provide essential ballistic protection; however, due to their construction and materials, they can affect the body's microclimate and result in heat stress. Whilst technical textiles and clever design can help to minimize this affect, the designer must decide whether protection or thermal comfort rank more highly in the user's hierarchy of needs. The design of fast-jet aircrew clothing well illustrates the complexity of high-performance apparel. Design criteria dictate that flight-survival gear is multifunctional and must protect wearers against both extreme heat and extreme cold. Garments must integrate and function with equipment in the cockpit (the primary use environment) and allow the addition and integration of survival equipment to the body-armor carrier vest worn over the flight suit, whilst providing thermal balance and avoiding snag hazards. Materials must be breathable and fabrics and components must be fire-retardant, nonmelt, antistatic, and anti-explosive spark. Flight clothing must be robust enough to survive ejection from the aircraft and adaptable to any environment in which the survivors find themselves on landing (currently facilitated through a seven-layer clothing system). Predominant posture is that of a seated position, accommodated through design features; however, the wearer must be able to function effectively whilst on the ground and in motion in extreme environments. This example demonstrates that most considerations are essential for survival and are highly ranked, while other criteria are of lesser importance, but must also be accommodated thus presenting a significant challenge to the designer in finding solutions such as providing warmth and protection, without adding weight and bulk.

13.1.3 Why Interactive Design Optimization?

The design process can be quite complex. Often the problem cannot be stated in a precise form for complete analysis and there are uncertainties in the design data. The solution to the problem need not exist. On many occasions, the formulation of the problem must be developed as part of the design process. Therefore, it is neither desirable nor useful to optimize an inexact problem to the end in a batch environment. It would be a complete waste of valuable resources to find out at the end that wrong data were used or a constraint was inadvertently omitted. It is desirable to have an interactive algorithm and software capable of designer interaction. Such a capability can be extremely useful in a practical design environment because not only can better designs be obtained, but more insights into the problem behavior can be gained. The problem formulation can be refined, and inadequate and absurd designs can be avoided. We shall describe some interactive algorithms and other suitable capabilities to demonstrate the usefulness of designer interaction in the design process.

1.4 The design cycle model: pre-design phase, design phase, and post-design phase

The design process—in the narrow sense—can be defined as the process of developing a design solution to fulfill external requirements or specifications3 (discussed in further detail in Chapter 9). In this book, we expand on this definition by looking at design as a cycle (Taura, 2014). We need a broader understanding of the design process before we can discuss the two questions mentioned in the previous section. Our model (referred to here as the design cycle model) consists of three phases: the pre-design phase, the design phase, and the post-design phase. This will be discussed in further detail in Chapter 2, but for now, the overview in Fig. 1.3 provides a brief introduction.

The pre-design phase refers to the phase when the concrete requirements or specifications for a new product that we might expect society to accept are generated on the basis of the motive of design. It is a “translation” process, during which the motive of design is translated into the requirements or specifications for the new product.

The design phase refers to the conventional design process. During this phase, structures and shapes are developed to satisfy the requirements or specifications for the new product. It could also be described as a process of “implementation,” during which the requirements or specifications are implemented into a specific form. Usually, the design phase refers only to the process of creating drawings or sketches. Here, however, we also include the process of manufacturing the actual product.

The post-design phase refers to the generation—explicitly or implicitly, and in society or individuals—of the motive of design, which will lead to future products. Not only do consumers use the product in accordance with its pre-defined usage, they also develop an understanding of the product and discover new uses or meanings for it. This phase could therefore be described as a process of “interpretation.”

The term “design,” as defined in Section 1.2 of this chapter, encompasses the pre-design and design phases of the design cycle model. The post-design phase is not a direct part of design, although it is closely related.

2.1 The design process in terms of probabilities

The design process can be understood as a process that decreases the degree of abstraction of the objects handled until finally reaching the maximum concreteness when the probability of success1 is within certain acceptable limits. The decisions made during the design process determine the probability of success because they choose one option and not another. It is natural for some options, which could initially appear very attractive, to have a greater probability of being discarded as the design process progresses. These ideas enable us to describe the design process as a temporal evolution of certain probability distributions. To see this, let’s begin with a simple situation.

Suppose that a department has been given the task of generating a set of tentative solutions to a particular design problem, and that it belongs to a larger design division. This department finds n possible solutions. These are named using a set of available labels, for example, the labels comprising the elements in set OX = {x1,x2,…,xn}. Obviously, all of these solutions have survived the same selection criteria present during the synthesis phase. There is therefore no clear argument in this department for selecting one over another. Under these conditions, all of the solutions are equally probable. Now, suppose there is a second department within the same design division, whose task is to choose a particular solution from the ones provided by the previous department. To do so, this department will have to use new criteria that were not available to the previous department. Let us assume that, having applied these criteria, they manage to rule out all of the solutions except two, which they believe to be equally feasible, for example, x2 and x4. If a third department were to assess and evaluate the two surviving solutions with new decision criteria, it would be able to select the best one; let’s say x2. A decision has been made in each of these steps, first restricting the set of feasible solutions from {x1,x2,…,xn} to {x2,x4}, and then to {x2}. The decision making has increased the certainty of the solution. It could initially be any value from set OX with the same probability, and finally only the value x2 with complete certainty. In general, this process can be interpreted as the evolution of a probability distribution.

Definition: We can say that the probability of element xi in set OX = {x1,x2,…,xn} is pi = Pr|OX(xi) ≥ 0, where Σi=1npi=1, if variable X with alphabet OX takes the value xi with the probability pi. Set PX = (p1,…,pn) containing the probabilities of each element is also called the probability distribution of variable X.

With this definition, the above design process, which first generated solution set {x1,x2,…,xn}, then selected {x2,x4}, and finally chose {x2}, can be interpreted as the evolution of the probability of the different elements making up the set: the output from the first department will be set OX with the uniform probability distribution PX(t1) = (1/n,…,1/n). As a result of the work of the second department, the above distribution is transformed into the probability distribution PX(t2) = (0,1/2,0,1/2,0, …,0). Finally, the resulting probability distribution will be PX(t3) = (0,1,0,…,0), where t3 > t2 > t1 are the instants of time in which each department finishes its work.

Note that nothing has been said about the nature of the decision-making tool. These can range from the purely random (for example, flipping a coin) to the most sophisticated, based on the execution of each tentative solution down to the last detail in order to check the degree of satisfaction produced. Obviously, both extremes are inefficient. The first consumes resources to produce a solution that may be far from what the customer expects, and the second consumes resources to produce n-1 solutions that will eventually be ruled out. It is the task of engineering and design science to establish the tools to optimize such decision making; in other words, to ensure the largest amount of useful information2 for correct decision making with the lowest resource consumption.

Be compatible with information systems technologies

The design process is essentially structured around a search of the defined solution spaces to identify established practices and design embodiments and of finding ways of combining these to produce a new or improved product or outcome. Increasingly, methods based around the use of expert systems and structured knowledge bases are providing the means by which the initial searches can be made. It is however important to recognise that the output from these searches should be treated, in the first instance at least, more as suggestions or advice than absolutes (El-Nakla and Bradley, 2008).

THE BIOCLIMATIC DESIGN

The design process developed according to the following rules of residential settlement bioclimatic design:

local climate, site and environment factors analysis,

study of housing layout and aggregation type for winter sun exposure and summer shading,

study of interior arrangement as regards comfort,

choice of low cost flexible building technique with appropriate thermophysical characteristics,

design of simple solar systems for passive climatization,

design of built-in solar roof collectors for domestic hot water,

design of highly efficient and reliable auxiliary heating system able to work in connection with the passive system.

The building site is bioclimatically excellent: facing SSE, slightly sloping about 12 per cent and protected from the North winds by the existing hills and vegetation (Fig. 1-2). This site feature ameliorates the local climate conditions, and the ground level higher then the close river Arno prevents the site from the persistent winter fog.

The local climate analysis presented some difficulties for the lack of close weather stations. So that a representative climate reference year has been generated by analizing the fortnightly average values of the surrounding weather stations, later corrected by direct observations of the local environment factors (Fig. 3-4). The housing layout on the ground and the optimum aggregation of the houses have been fixed to get maximum winter sunshine.

This operation has been done during design process by means of some sectional models, lighted by a lamp that simulates the sun position in winter, within ±45° degrees azimuth from South direction (Fig. 5). The approved housing layout consists of 5 blocks, two and three floors high, disposed E–W with wide fronts facing the South, spaced enough to avoid shadows.

Each block is a sort of row–house aggregation, whose shape and interior space arrangement have been conceived to get high solar heat gain in winter and good indoor comfort in summer.

So the deep livingrooms and the main bedrooms have wide windows facing the South to let the winter sun penetrate deeply into the room; the heat collected is distributed in the other rooms by indoor ventilation.

On summer afternoons, the South windows are shadowed by the staggered front and balconies (Fig 6–7–8), and interior comfort is improved by cross ventilation, induced by the small windows facing the North.

The direct gain system for passive climatization has been chosen for its simplicity of realization and management, after several technical and economic evaluations, run by the designers the client and the contractor together. Especially the criterion prevailed to give the user a simple and durable passive system to manage.

Small rooms and facilities are sited on the North side.

The staircase is built externally on the North side, to act as a shelter for winter wind.

A flexible and low cost building technique, with appropriate thermophysical performances has been chosen. The outer walls are made of insulated blocks, with polystyrene inserted in the exterior cavity, to increase the U–value and the available interior heat storage at the same time (Fig, 9-10).

The good performance of this technique has been estimated by computer numerical simulation of the quantity of auxiliary energy, to be supplied during 50 winter days of the local reference-year for three different insulation techniques (Fig. 11).

Coverings, terraces and groundfloors are highly insulated with polystyrene and foam cement, and the average G-value of each block is 0.824 Wm−3°C−1.

Auxiliary heating is supplied by a traditional system, equipped with individual gas furnace and heat radiators. Differential thermoregulation is provided in each sunny room by thermostatic valves, and a central thermostat with nightly reduction is provided too. This simple solution has proved to be conveniently energy saving at low cost of realization, and fitting to act promptly in connection with the direct gain passive system.

6.14.1.4 Design, Analysis, and Test for Composites on Spacecraft

Any design process begins with the mission/system/subsystem requirements as they are reduced to structural design criteria such as weight, launch acceleration, and minimum acceptable natural vibration frequency. For construction and support of optical and antenna components, the acceptable limits on thermal distortion are specified. The design sequence for spacecraft is similar to an aircraft design process. For unique components, design of the manufacturing tools and fixtures may be a key part of the process. Structural analyses are also generally similar to aircraft practice, with a stronger reliance on vibration analysis to predict natural frequencies. Additional special analyses include thermal cycling associated with the periodic passage through the earth's shadow in orbit and thermoelastic calculation of the displacements of optical supports.

Structural testing usually does not include building a full-scale structural model for destructive testing; instead, full-scale articles are structurally tested and eventually configured for launch. Subscale or partial components known as “pathfinders” are fabricated and tested to validate new designs and new fabrication procedures. For a typical low production (one to three of a kind) spacecraft, coupons are routinely cut from the edges/scraps of the production parts and subsequently tested to ensure that the material properties are consistent with the design values.

How Do I Begin to Design a System?

Designing engineering systems can be a complex process. Assumptions must be made to develop realistic models that can be subjected to mathematical analysis by the available methods. The models may need to be verified by experiments. Many possibilities and factors must be considered during the optimization problem formulation phase. Economic considerations play an important role in designing cost-effective systems. To complete the design of an engineering system, designers from different fields of engineering must usually cooperate. For example, the design of a high-rise building involves designers from architectural, structural, mechanical, electrical, and environmental engineering, as well as construction management experts. Design of a passenger car requires cooperation among structural, mechanical, automotive, electrical, chemical, hydraulics design, and human factor engineers. Thus, in an interdisciplinary environment, considerable interaction is needed among design teams to complete the project. For most applications, the entire design project must be broken down into several subproblems, which are then treated somewhat independently. Each of the subproblems can be posed as a problem of optimum design.

The design of a system begins with the analysis of various options. Subsystems and their components are identified, designed, and tested. This process results in a set of drawings, calculations, and reports with the help of which the system can be fabricated. We use a systems engineering model to describe the design process. Although complete discussion of this subject is beyond the scope of this text, some basic concepts are discussed using a simple block diagram.

Design is an iterative process. Iterative implies analyzing several trial designs one after another until an acceptable design is obtained. It is important to understand the concept of a trial design. In the design process, the designer estimates a trial design of the system based on experience, intuition, or some simple mathematical analyses. The trial design is then analyzed to determine if it is acceptable. In case it gets accepted, the design process is terminated. In the optimization process, the trial design is analyzed to determine if it is the best. Depending on the specifications, “best” can have different connotations for different systems. In general, it implies that a system is cost-effective, efficient, reliable, and durable. The basic concepts are described in this text to aid the engineer in designing systems at minimum cost.

The design process should be well organized. To discuss it, we consider a system evolution model, shown in Fig. 1.1, where the process begins with the identification of a need that may be conceived by engineers or nonengineers. The five steps of the model in the figure are described in the following paragraphs.

The first step in the evolutionary process is to precisely define the specifications for the system. Considerable interaction between the engineer and the sponsor of the project is usually necessary to quantify the system specifications.

The second step in the process is to develop a preliminary design of the system. Various system concepts are studied. Since this must be done in a relatively short time, simplified models are used at this stage. Various subsystems are identified and their preliminary designs are estimated. Decisions made at this stage generally influence the system’s final appearance and performance. At the end of the preliminary design phase, a few promising design concepts that need further analysis are identified.

The third step in the process is a detailed design for all subsystems using the iterative process described earlier. To evaluate various possibilities, this must be done for all previously identified promising design concepts. The design parameters for the subsystems must be identified. The system performance requirements must be identified and formulated. The subsystems must be designed to maximize system worth or to minimize a measure of the cost. Systematic optimization methods described in this text aid the designer in accelerating the detailed design process. At the end of the process, a description of the final design is available in the form of reports and drawings.

The fourth and fifth steps shown in Fig. 1.1 may or may not be necessary for all systems. They involve fabrication of a prototype system and testing, and are necessary when the system must be mass-produced or when human lives are involved. These steps may appear to be the final ones in the design process, but they are not because the system may not perform according to specifications during the testing phase. Therefore, the specifications may have to be modified or other concepts may have to be studied. In fact, this reexamination may be necessary at any point during the design process. It is for this reason that feedback loops are placed at every stage of the system evolution process, as shown in Fig. 1.1. This iterative process must be continued until the best system evolves. Depending on the complexity of the system, this process may take a few days or several months.

The model described in Fig. 1.1 is a simplified block diagram for system evolution. In actual practice, each block may be broken down into several subblocks to carry out the studies properly and arrive at rational decisions. The important point is that optimization concepts and methods are helpful at every stage of the process. Such methods, along with the appropriate software, can be useful in studying various design possibilities rapidly.

3.2.1 The design process

The design process usually starts with a requirement or desire for a new item or product. Research will usually be carried out then to find out as much as possible about this need and about the role or function the new item or product is to have. Ideas generation is the next stage when various alternative initial ideas are conceived. These initial ideas are then usually developed through until the designer is happy to offer them as proposals to meet the initial need. In the early stages, alternative ideas will often also be presented. These proposals will be considered and perhaps modified. A decision is then taken as to the best solution to the design problem and the necessary specifications and instructions will then be given. (See Fig. 3.1.)

3.2 Design Process and Levels of Abstraction

The design process for a technical product may, in general, be roughly subdivided into three main phases: conceptualization, concept refinement, and implementation. Inputs to the process are the design requirements, eventually resulting in the design results. A more detailed view to this “black box” model of the design process, as depicted in Figure 3.1, results in an identification of a process chain consisting of generating and analyzing activities to be performed iteratively in the outlined steps of conceptualization, refinement, and implementation.

The design requirements consist of a description of the envisaged functionality of the new product and sometimes of constraints referring to the final implementation, such as an exploitation of commercially available subsystems. This set of information is commonly known as the technical product specification, and it is still denoted in an informal way (i.e., written in a natural language and augmented by some tables and diagrams). Nontechnical specifications such as cost frames and design deadlines are important for the design process as well. They are, therefore, viewed as additional inputs to the design process as outlined in Figure 3.1.

The first and most important activities of the systems engineer during the conceptual phase are formalization of the specification, determination of solution strategies, and partitioning of the overall task into independent subtasks. These tasks are forwarded to design teams specializing in different areas, such as analog circuit design or real-time software engineering. Figure 3.2 depicts the design flow during conceptualization and refinement.

Modeling plays a central role in this design phase. Different model instantiations, abstraction levels, and accuracy requirements have to be dealt with during concept refinement. In addition, multinature systems in general operate time-continuously, but the information processing inherent to most such systems consists of digital hardware and software modules, which are best represented in a time- or even-discrete way. Levels of abstraction are well-defined in the digital domain. They are summarized in Table 3.1.

TABLE 3.1. Levels of Abstraction in Digital Systems

Views, abstraction levels, modeling concepts, structural primitives, and time models are related to the observable values produced by models of digital systems. These models may be denoted in a hardware description language such as Verilog or VHDL. We will focus on the latter in the following paragraphs for reasons given in the next section.

Abstraction levels for time-continuous systems are not yet that well agreed upon in terms of counterparts for digital circuits and systems. Table 3.2 introduces and discusses four levels of design concepts and observable signals beginning with the highest abstraction level.

TABLE 3.2. Levels of Abstraction in Analog Systems

These abstraction levels seem to be strongly related to analog circuit design only—especially levels three and four. However, an introduction of the abstraction levels one and two—functional and behavioral—is well suited for modeling purposes in other engineering domains as demonstrated in the last section of this chapter.

The interrelation between conceptual models according to Figure 3.3 and the executing simulator is highly complex. An appropriate support of model representations is thus mandatory. This is covered by highly expressive modeling languages such as VHDL–AMS, which originated recently from the well-known hardware description language VHDL aimed at denoting executable models of digital systems. Figure 3.4 highlights this relationship.